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#include <omph> Int const NPOP // number of chromosomes ("40000) Int const NCHROME // length of each chromosome Real :: tempScalar Array of Real :: temp(NCHROME) Array of Int :: iparent(NCHROME, NPOP) Array of Int :: fitness(NPOP) Int :: j, iother #pragma omp parallel for loop [j] over NPOP iother = par_rand(j) // returns random value greater // than or equal to zero but not // equal to j and less than NPOP #pragma omp critical { // Swap Chromosomes

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for example, idle3 -d -r statisticspy sumdat The -d argument tells IDLE to start debugging immediately and the -r argument tells it to run the following program with any arguments that follow it However, for programs that don t require command-line arguments (or where we are willing to edit the code to put them in manually to make debugging easier), IDLE is quite powerful and convenient to use (Incidentally, the code shown in Figure 92 does have a bug middle + 1 should be middle - 1) Debugging Python programs is no harder than debugging in any other language and it is easier than for compiled languages since there is no build step to go through after making changes And if we are careful to use the scienti c method it is usually quite straightforward to locate bugs, although xing them is another matter Ideally, though, we want to avoid as many bugs as possible in the rst place And apart from thinking deeply about our design and writing our code with care, one of the best ways to prevent bugs is to use TDD, a topic we will introduce in the next section

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Writing tests for our programs if done well can help reduce the incidence of bugs and can increase our con dence that our programs behave as expected But in general, testing cannot guarantee correctness, since for most nontrivial programs the range of possible inputs and the range of possible computations is so vast that only the tiniest fraction of them could ever be realistically tested Nonetheless, by carefully choosing what we test we can improve the quality of our code A variety of different kinds of testing can be done, such as usability testing, functional testing, and integration testing But here we will concern ourselves

temp(1:NCHROME) = iparent(1:NCHROME, iother) iparent(1:NCHROME, iother) = iparent(1:NCHROME, j) iparent(1:NCHROME, j) = temp(1:NCHROME) // Swap fitness metrics tempScalar = fitness(iother) fitness(iother) = fitness(j) fitness(j) = tempScalar } end loop [j]

purely with unit testing testing individual functions, classes, and methods, to ensure that they behave according to our expectations A key point of TDD, is that when we want to add a feature for example, a new method to a class we rst write a test for it And of course this test will fail since we haven t written the method Now we write the method, and once it passes the test we can then rerun all the tests to make sure our addition hasn t had any unexpected side effects Once all the tests run (including the one we added for the new feature), we can check in our code, reasonably con dent that it does what we expect providing of course that our test was adequate For example, if we want to write a function that inserts a string at a particular index position, we might start out using TDD like this:

def insert_at(string, position, insert): """Returns a copy of string with insert inserted at the position >>> string = "ABCDE" >>> result = [] >>> for i in range(-2, len(string) + 2): resultappend(insert_at(string, i, "-")) >>> result[:5] ['ABC-DE', 'ABCD-E', '-ABCDE', 'A-BCDE', 'AB-CDE'] >>> result[5:] ['ABC-DE', 'ABCD-E', 'ABCDE-', 'ABCDE-'] """ return string

Payments, which includes the subtypes CreditCards, GiftCards, and BankAccounts Let s first look at Customers, which on the surface looks like an entity that would work as a single table In reality, the company wants to be able to store multiple addresses for each customer If you look at our model, we can store only a single address for each customer To remedy this, we create a new table, named tbl_address, remove the address information from tbl_customer, and set up a relationship between the two tables The resulting address and customer tables are shown in Figure 98

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